AI-Representative (TÜV).
- Seminar
- Präsenz / Virtual Classroom
- Zurzeit keine Termine
- 24 Unterrichtseinheiten
- Bedienberechtigung
AI Representatives plan, assess, and manage AI projects, ensuring strategic fit, ethics, and legal compliance.
An AI Representative is responsible for initiating and managing AI projects. This person works in close coordination with the management who have initiated an AI strategy and implements it. His job is to analyzes the potential of AI applications in the company and communicates these to the management level together with the requirements.
This course is also available in german language and is named KI-Beuaftrafter (TÜV).
Nutzen
- You will gain an overview of the opportunities and risks of using AI-driven applications.
- You will learn about the criteria for selecting AI-driven applications.
- You will learn about compliance risks relating to the EU AI Regulation (AI Act), copyright, data protection, etc. and how to avoid them.
- Get an overview of common project management methods
- Learn how to set up an AI project organizationally and formally
- Understand the special features of AI projects
- Create a fictitious roadmap as a template for your own first AI project
Zielgruppe
IT-savvy employees who initiate, manage and evaluate AI projects. The task is to improve processes with the help of AI integration. To this end, the AI officer analyzes existing systems for their transformation potential, designs overarching AI strategies and implements or supports the necessary projects.
Inhalte
Participants are prepared to launch and successfully implement AI projects in the company.
The AI officer is responsible for initiating and managing AI projects and therefore works in close coordination with the decision-makers at management level who have initiated an AI strategy.
In addition to analyzing the potential of AI applications in the company, the special features of AI projects and the associated legal challenges and compliance risks must be taken into account.
AI compliance & legal classification (8 units)
- Definition of "artificial intelligence" and "machine learning"
- Market overview and application examples of AI
- The EU AI Regulation (AI Act)
- Data protection challenges of AI
- Copyright challenges of AI
- Liability issues (damages, product liability, general compliance)
- Future general effects of AI-driven applications on the economic and working world
Basics of project management (1 unit)
- Definition of projects
- The role of the project manager
- Other roles in projects
Project management methods (3 units)
- Agile vs. classic projects
- Scrum
- Kanban
- Waterfall
- Comparison of the methods
Stakeholder management (1 UE)
- Importance of stakeholder management
- Identification of stakeholders
- techniques
Project setup (3 units)
- Project assignment
- Work breakdown structure
- Project planning
- Resource management
Special challenges of AI projects (2 units)
- Risk management
- Quality assurance
- Expectation management
- Model selection
- Computing resources
The right project team (1 UE)
- Requirements for the project team in AI projects
Data management in AI projects (1 TU)
- Data acquisition and preparation
- Data quality and cleansing
Creating a roadmap (3 units)
- Project planning
- Phases and milestones in AI projects
- Practical application
Scaling (1 unit)
- From proof of concept to productive system
-
- Scrum of Scrum
- LeSS
- SAFe
PersCert final exam (60 min)
